(1) Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation.
(2) New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders cannot only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development.
(3) Recommender technology can assist Social Web systems through increasing adoption and participation and sustaining membership. Through targeted and timely intervention which stimulates traffic and interaction, recommender technology can play its role in sustaining the success of the Social Web.
(4) The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people and can require more interactive and richer recommender systems user interfaces.
The technical papers appearing in these proceedings aim to explore and understand challenges and new opportunities for recommender systems in the Social Web and were selected in a formal review process by an international program committee.
Overall, we received 13 paper submissions from 12 different countries, out of which 7 long papers and 1 short paper were selected for presentation and inclusion in the proceedings. The submitted papers addressed a variety of topics related to Social Web recommender systems from the use of microblogging data for personalization over new tag recommendation approaches to social media-based personalization of news.}, address = {New York, NY, USA}, author = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Hotho, Andreas}, interhash = {4a591caf39ca41da55a94a37c8c47074}, intrahash = {354947709c23c90b18dae862c46b2761}, isbn = {978-1-4503-1638-5}, location = {Dublin, Ireland}, note = 609126, publisher = {ACM}, title = {RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, year = 2012 } @incollection{gemmell2010resource, abstract = {Collaborative tagging applications enable users to annotate online resources with user-generated keywords. The collection of these annotations and the way they connect users and resources produce a rich information space for users to explore. However the size, complexity and chaotic structure of these systems hamper users as they search for information. Recommenders can assist the user by suggesting resources, tags or even other users. Previous work has demonstrated that an integrative approach which exploits all three dimensions of the data (users, resources, tags) produce superior results in tag recommendation. We extend this integrative philosophy to resource recommendation. Specifically, we propose an approach for designing weighted linear hybrid resource recommenders. Through extensive experimentation on two large real world datasets, we show that the hybrid recommenders surpass the effectiveness of their constituent components while inheriting their simplicity, computational efficiency and explanatory capacity. We further introduce the notion of information channels which describe the interaction of the three dimensions. Information channels can be used to explain the effectiveness of individual recommenders or explain the relative contribution of components in the hybrid recommender.}, address = {Berlin/Heidelberg}, affiliation = {Center for Web Intelligence, School of Computing, DePaul University, Chicago, Illinois USA}, author = {Gemmell, Jonathan and Schimoler, Thomas and Mobasher, Bamshad and Burke, Robin}, booktitle = {E-Commerce and Web Technologies}, doi = {10.1007/978-3-642-15208-5_1}, editor = {Buccafurri, Francesco and Semeraro, Giovanni}, interhash = {357183305397b19624ec246b915df6ac}, intrahash = {684579385b3a4f90f5b41ce7c92ddb2a}, isbn = {978-3-642-15208-5}, keyword = {Computer Science}, pages = {1--12}, publisher = {Springer}, series = {Lecture Notes in Business Information Processing}, title = {Resource Recommendation in Collaborative Tagging Applications}, url = {http://dx.doi.org/10.1007/978-3-642-15208-5_1}, volume = 61, year = 2010 } @article{gemmell2012resource, abstract = {Social annotation systems enable the organization of online resources with user-defined keywords. Collectively these annotations provide a rich information space in which users can discover resources, organize and share their finds, and connect to other users with similar interests. However, the size and complexity of these systems can lead to information overload and reduced utility for users. For these reasons, researchers have sought to apply the techniques of recommender systems to deliver personalized views of social annotation systems. To date, most efforts have concentrated on the problem of tag recommendation – personalized suggestions for possible annotations. Resource recommendation has not received the same systematic evaluation, in part because the task is inherently more complex. In this article, we provide a general formulation for the problem of resource recommendation in social annotation systems that captures these variants, and we evaluate two cases: basic resource recommendation and tag-specific resource recommendation. We also propose a linear-weighted hybrid framework for resource recommendation. Using six real-world datasets, we show that its integrative approach is essential for this recommendation task and provides the most adaptability given the varying data characteristics in different social annotation systems. We find that our algorithm is more effective than other more mathematically-complex techniques and has the additional advantages of flexibility and extensibility.}, author = {Gemmell, Jonathan and Schimoler, Thomas and Mobasher, Bamshad and Burke, Robin}, doi = {10.1016/j.jcss.2011.10.006}, interhash = {e7a4b630500c6a468c40d0e63ee31455}, intrahash = {de0e3910bd4932b63e5ba6058e5cee45}, issn = {0022-0000}, journal = {Journal of Computer and System Sciences}, number = 4, pages = {1160 - 1174}, title = {Resource recommendation in social annotation systems: A linear-weighted hybrid approach}, url = {http://www.sciencedirect.com/science/article/pii/S0022000011001127}, volume = 78, year = 2012 } @inproceedings{shepitsen2008personalized, abstract = {Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.}, acmid = {1454048}, address = {New York, NY, USA}, author = {Shepitsen, Andriy and Gemmell, Jonathan and Mobasher, Bamshad and Burke, Robin}, booktitle = {Proceedings of the 2008 ACM conference on Recommender systems}, doi = {10.1145/1454008.1454048}, interhash = {c9028129dd7cd8314673bd64cbb6198e}, intrahash = {0700627147554148d7e6db5979aa27d2}, isbn = {978-1-60558-093-7}, location = {Lausanne, Switzerland}, numpages = {8}, pages = {259--266}, publisher = {ACM}, series = {RecSys '08}, title = {Personalized recommendation in social tagging systems using hierarchical clustering}, url = {http://doi.acm.org/10.1145/1454008.1454048}, year = 2008 } @inproceedings{gemmell2009improving, abstract = {Collaborative tagging applications allow users to annotate online resources. The result is a complex tapestry of interrelated users, resources and tags often called a folksonomy. Folksonomies present an attractive target for data mining applications such as tag recommenders. A challenge of tag recommendation remains the adaptation of traditional recommendation techniques originally designed to work with two dimensional data. To date the most successful recommenders have been graph based approaches which explicitly connects all three components of the folksonomy. In this paper we speculate that graph based tag recommendation can be improved by coupling it with item-based collaborative filtering. We motive this hypothesis with a discussion of informational channels in folksonomies and provide a theoretical explanation of the additive potential for item-based collaborative filtering. We then provided experimental results on hybrid tag recommenders built from graph models and other techniques based on popularity, user-based collaborative filtering and item-based collaborative filtering. We demonstrate that a hybrid recommender built from a graph based model and item-based collaborative filtering outperforms its constituent recommenders. furthermore the inability of the other recommenders to improve upon the graph-based approach suggests that they offer information already included in the graph based model. These results confirm our conjecture. We provide extensive evaluation of the hybrids using data collected from three real world collaborative tagging applications.}, author = {Gemmell, Jonathan and Schimoler, Thomas R. and Christiansen, Laura and Mobasher, Bamshad}, booktitle = {ACM RecSys'09 Workshop on Recommender Systems and the Social Web}, editor = {Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Anand, Sarabjot Singh and Dugan, Casey and Mobasher, Bamshad and Kobsa, Alfred}, interhash = {0900f921d87c5ee19a4ed2c70e5a71df}, intrahash = {6b1ff3b7b691b84288fb7122968134c4}, issn = {1613-0073}, month = oct, pages = {17--24}, series = {CEUR-WS.org}, title = {Improving Folkrank With Item-Based Collaborative Filtering}, url = {http://ceur-ws.org/Vol-532/paper3.pdf}, volume = 532, year = 2009 } @inproceedings{shepitsen2008personalized, abstract = {Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.}, address = {New York, NY, USA}, author = {Shepitsen, Andriy and Gemmell, Jonathan and Mobasher, Bamshad and Burke, Robin}, booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems}, doi = {http://doi.acm.org/10.1145/1454008.1454048}, interhash = {c9028129dd7cd8314673bd64cbb6198e}, intrahash = {a7552f8d8d5db4f867ae6e94e1a4442f}, isbn = {978-1-60558-093-7}, location = {Lausanne, Switzerland}, pages = {259--266}, publisher = {ACM}, title = {Personalized recommendation in social tagging systems using hierarchical clustering}, url = {http://portal.acm.org/citation.cfm?id=1454008.1454048}, year = 2008 } @article{gemmell2008personalizing, abstract = {The popularity of collaborative tagging, otherwise known as “folksonomies�?, emanate from the flexibility they afford usersin navigating large information spaces for resources, tags, or other users, unencumbered by a pre-defined navigational orconceptual hierarchy. Despite its advantages, social tagging also increases user overhead in search and navigation: usersare free to apply any tag they wish to a resource, often resulting in a large number of tags that are redundant, ambiguous,or idiosyncratic. Data mining techniques such as clustering provide a means to overcome this problem by learning aggregateuser models, and thus reducing noise. In this paper we propose a method to personalize search and navigation based on unsupervisedhierarchical agglomerative tag clustering. Given a user profile, represented as a vector of tags, the learned tag clustersprovide the nexus between the user and those resources that correspond more closely to the user’s intent. We validate thisassertion through extensive evaluation of the proposed algorithm using data from a real collaborative tagging Web site.}, author = {Gemmell, Jonathan and Shepitsen, Andriy and Mobasher, Bamshad and Burke, Robin}, file = {gemmell2008personalizing.pdf:gemmell2008personalizing.pdf:PDF}, groups = {public}, interhash = {e544ba095f411429896b11fd3f94fd5c}, intrahash = {2e0535788c372e98e49646873cea4e1e}, journal = {Data Warehousing and Knowledge Discovery}, journalpub = {1}, pages = {196--205}, timestamp = {2009-08-10 10:30:08}, title = {Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering}, url = {http://dx.doi.org/10.1007/978-3-540-85836-2_19}, username = {dbenz}, year = 2008 } @inproceedings{marinho:ecml2009, abstract = {Collaborative tagging applications allow users to annotate online resources, resulting in a complex three dimensional network of interrelated users, resources and tags often called a folksonom A pivotal challenge of these systems remains the inclusion of the varied information channels introduced by the multi-dimensional folksonomy into recommendation techniques. In this paper we propose a composite tag recommender based upon popularity and collaborative filtering. These recommenders were chosen based on their speed, memory requirements and ability to cover complimentary channels of the folksonomy. Alone these recommenders perform poorly; together they achieve a synergy which proves to be as effective as state of the art tag recommenders.}, address = {Bled, Slovenia}, author = {Gemmell, Jonathan and Ramezani, Maryam and Schimoler, Thomas and Christiansen, Laura and Mobasher, Bamshad}, booktitle = {ECML PKDD Discovery Challenge 2009 (DC09)}, editor = {Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert}, interhash = {4c9f99e93a8038baad43493c0ba8600f}, intrahash = {cea0f6c4149738eb084852aa6c71b935}, issn = {1613-0073}, month = {September}, publisher = {CEUR Workshop Proceedings}, title = {A Fast Effective Multi-Channeled Tag Recommender}, url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-497/}, volume = 497, year = 2009 } @inproceedings{gemmell2009impact, abstract = {Collaborative tagging applications have become a popular tool allowing Internet users to manage online resources with tags. Most collaborative tagging applications permit unsupervised tagging resulting in tag ambiguity in which a single tag has many different meanings and tag redundancy in which several tags have the same meaning. Common metrics for evaluating tag recommenders may overestimate the utility of ambiguous tags or ignore the appropriateness of redundant tags. Ambiguity and redundancy may even burden the user with additional effort by requiring them to clarify an annotation or forcing them to distinguish between highly related items. In this paper we demonstrate that ambiguity and redundancy impede the evaluation and performance of tag recommenders. Five tag recommendation strategies based on popularity, collaborative filtering and link analysis are explored. We use a cluster-based approach to define ambiguity and redundancy and provide extensive evaluation on three real world datasets.}, address = {New York, NY, USA}, author = {Gemmell, Jonathan and Ramezani, Maryam and Schimoler, Thomas and Christiansen, Laura and Mobasher, Bamshad}, booktitle = {RecSys '09: Proceedings of the third ACM conference on Recommender systems}, doi = {http://doi.acm.org/10.1145/1639714.1639724}, interhash = {15c65045bac07bf5f9f82526aabf716b}, intrahash = {0710acde8c3db11f5dbd63f76bd30dc6}, isbn = {978-1-60558-435-5}, location = {New York, New York, USA}, pages = {45--52}, publisher = {ACM}, title = {The impact of ambiguity and redundancy on tag recommendation in folksonomies}, url = {http://portal.acm.org/citation.cfm?id=1639724}, year = 2009 } @inproceedings{conf/ijcai/GemmellSRM09, author = {Gemmell, Jonathan and Schimoler, Thomas and Ramezani, Maryam and Mobasher, Bamshad}, booktitle = {ITWP}, crossref = {conf/ijcai/2009itwp}, editor = {Anand, Sarabjot S. and Mobasher, Bamshad and Kobsa, Alfred and Jannach, Dietmar}, ee = {http://ceur-ws.org/Vol-528/paper8.pdf}, interhash = {9ce91fd3f0808eb4b750e0d1d68bdaf0}, intrahash = {8b50a08149b62c6fed95fd6e557f89bf}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Adapting K-Nearest Neighbor for Tag Recommendation in Folksonomies.}, url = {http://dblp.uni-trier.de/db/conf/ijcai/itwp2009.html#GemmellSRM09}, volume = 528, year = 2009 } @inproceedings{shepitsen2008personalized, abstract = {Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.}, address = {New York, NY, USA}, author = {Shepitsen, Andriy and Gemmell, Jonathan and Mobasher, Bamshad and Burke, Robin}, booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems}, doi = {10.1145/1454008.1454048}, interhash = {c9028129dd7cd8314673bd64cbb6198e}, intrahash = {a7552f8d8d5db4f867ae6e94e1a4442f}, isbn = {978-1-60558-093-7}, location = {Lausanne, Switzerland}, pages = {259--266}, publisher = {ACM}, title = {Personalized recommendation in social tagging systems using hierarchical clustering}, url = {http://portal.acm.org/citation.cfm?id=1454008.1454048}, year = 2008 } @inproceedings{Cooley97, author = {Cooley, R. and Mobasher, B. and Srivastava, J.}, booktitle = {Proceedings of the 9th International Conference on Tools with Artificial Intelligence (ICTAI'97)}, interhash = {94895d7c0cc214ed623d941b2dab7367}, intrahash = {e1a677620e58ec56e683e2a80c4f0feb}, month = {November}, pages = {558--567}, publisher = {IEEE Computer Society}, title = {Web mining : Information and pattern discovery on the world wide web}, year = 1997 } @article{Cooley99, author = {Cooley, R. and Mobasher, B. and Srivastava, J.}, interhash = {68b1e11110e6498699524008fe67f8c1}, intrahash = {8673b93f2e415df95099fe00bccd154d}, journal = {Knowledge and Information Systems}, month = {Febuary}, number = 1, pages = {5--32}, publisher = {Springer-Verlag}, title = {Data preparation for mining world wide web browsing patterns}, volume = 1, year = 1999 } @inproceedings{content-only, author = {Cooley, R. and Mobasher, B. and Srivastava, J.}, booktitle = {Proceedings of the Ninth IEEE International Conference on Tools with Artificial Intelligence (ICTAI'97)}, interhash = {94895d7c0cc214ed623d941b2dab7367}, intrahash = {e385cc03235ad1efc751e12fb2fd11d0}, location = {Newport Beach, CA}, month = Nov, publisher = {IEEE Computer Society}, title = {Web Mining: Information and Pattern Discovery on the World Wide Web}, url = {http://maya.cs.depaul.edu/~mobasher/papers/webminer-tai97.ps}, year = 1997 } @misc{ieKey, author = {Boley, Daniel and Gini, Maria and Gross, Robert and Han, Eui-Hong (Sam) and Hastings, Kyle and Karypis, George and Kumar, Vipin and Mobasher, Bamshad and Moore, Jerome}, date = {1999}, interhash = {d544ef5463da700ac7209b61b5bc7eef}, intrahash = {1a1d7962e0dbc3b0afac99911db093e1}, journal = {To appear in Decision Support Systems Journal}, title = {"Partitioning-Based Clustering for Web Document Categorization}, year = 1999 } @article{han98hypergraph, author = {Han, Eui-Hong and Karypis, George and Kumar, Vipin and Mobasher, Bamshad}, interhash = {3bb7fb3fd3af41fac2db5460a5acfd2c}, intrahash = {9723b092d975dedb8f6d5f711bb00ffd}, journal = {Data Engineering Bulletin}, number = 1, pages = {15-22}, title = {Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results}, url = {http://citeseer.ist.psu.edu/han98hypergraph.html}, volume = 21, year = 1998 }